Open AccessSpatially adaptive total variation deblurring with split Bregman technique
Author(s) -
Dodangeh Mahdi,
Figueiredo Isabel N.,
Gonçalves Gil
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0302
Subject(s) - deblurring , deconvolution , convolution (computer science) , variation (astronomy) , image (mathematics) , mathematics , algorithm , artificial intelligence , pattern recognition (psychology) , computer science , image quality , fourier transform , image processing , image restoration , mathematical analysis , physics , astrophysics , artificial neural network
In this study, the authors describe a modified non‐blind and blind deconvolution model by introducing a regularisation parameter that incorporates the spatial image information. Indeed, they have used a weighted total variation term, where the weight is a spatially adaptive parameter based on the image gradient. The proposed models are solved by the split Bregman method. To handle adequately the discrete convolution transform in a moderate time, fast Fourier transform is used. Tests are conducted on several images, and for assessing the results, they define appropriate weighted versions of two standard image quality metrics. These new weighted metrics clearly highlight the advantage of the spatially adaptive approach.